Proofreading of pre-40S ribosome maturation by a translation initiation factor and 60S subunits

In the final steps of yeast ribosome synthesis, immature translation-incompetent pre-40S particles that contain 20S pre-rRNA are converted to the mature translation-competent subunits containing the 18S rRNA. An assay for 20S pre-rRNA cleavage in purified pre-40S particles showed that cleavage by the PIN domain endonuclease Nob1 was strongly stimulated by the GTPase activity of the cytoplasmic translation initiation factor eIF5b/Fun12. Cleavage of the 20S pre-rRNA was also inhibited in vivo and in vitro by blocking binding of Fun12 to the 25S rRNA through specific methylation of its binding site. Cleavage competent pre-40S particles stably associate with Fun12 and form 80S complexes with 60S ribosomal subunits. We propose that recruitment of 60S subunits promotes GTP-hydrolysis by Fun12, leading to structural rearrangements within the pre-40S particle that bring Nob1 and the pre-rRNA cleavage site together

Online Multi-modal Learning and Adaptive Informative Trajectory Planning for Autonomous Exploration

In robotic information gathering missions, scientists are typically interested in understanding variables which require proxy measurements from specialized sensor suites to estimate. However, energy and time constraints limit how often these sensors can be used in a mission. Robots are also equipped with cheaper to use navigation sensors such as cameras. In this paper, we explore a challenging planning problem in which a robot is required to learn about a scientific variable of interest in an initially unknown environment by planning informative paths and deciding when and where to use its sensors. To tackle this we present two innovations: a Bayesian generative model framework to automatically learn correlations between expensive science sensors and cheaper to use navigation sensors online, and a sampling based approach to plan for multiple sensors while handling long horizons and budget constraints. Our approach does not grow in complexity with data and is anytime making it highly applicable to field robotics. We tested our approach extensively in simulation and validated it with real data collected during the 2014 Mojave Volatiles Prospector Mission. Our planning algorithm performs statistically significantly better than myopic approaches and at least as well as a coverage-based algorithm in an initially unknown environment while having added advantages of being able to exploit prior knowledge and handle other intricacies of the real world without further algorithmic modifications